4
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
Land of
Opportunity?
Economic Mobility in the United States
By Kartik Athreya and Jessie Romero
T
he gap between people in the highest percentiles of earnings and wealth
distributions and the rest of society has grown significantly during the
past several decades, a fact that has led to considerable public discussion
about the nature of opportunities available in the United States. Often overlooked in
this debate, however, is the importance of economic mobility—the extent to which
people are able to move up and down the income
ladder—in determining what inequality implies for
opportunity. If mobility is high, for example, the level of
inequality at any point in time is not necessarily cause
for concern, since it’s possible that today’s poor will be
tomorrow’s rich. The potential for such upward mobility is the foundation of the American dream that has
lured generations of immigrants to the United States.
The dream endures today. Nearly half of Americans
aged 18–29 believe they will become rich at some point
in their lifetimes, according to a 2012 Gallup Poll. But the
odds are against them: In 2010 (the most recent year
for which the Internal Revenue Service has published
data), only about 5 percent of U.S. households earned
more than $150,000 per year, and about 1 percent
earned more than $350,000 per year. (See Figure 1).
Most of those people, moreover, were not born to poor
parents—especially not in recent years.
Understanding economic mobility is essential to
understanding how observed levels and patterns of
economic inequality relate to the implicit promise of
American life. But this is complicated. Mobility and
inequality are determined jointly by random chance,
by policy, and—most confounding of all for social scientists—by the deliberate actions of individuals or their
parents. Regarding the latter determinant, it is clear
that people differ according to their aptitude for various tasks, their appetite for risk, and their preferences
for work versus leisure, among other characteristics.
Both mobility and inequality thus will arise at least in
part because different people make different choices.
(See sidebar on page 15.)
This reality creates a challenge for economists seeking to understand the sources of observed levels of
mobility and inequality, and for policymakers who
hope to influence those levels. If everyone has the
5
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
Figure 1: Thresholds for Selected Income Percentiles
THRESHOLDS FOR SELECTED INCOME PERCENTILES
$450,000
400,000
350,000
Top 1 percent
The gap between the
top 1 percent and all
other percentiles has
increased substantially.
Income
300,000
250,000
200,000
Top 5 percent
150,000
Top 10 percent
100,000
Top 25 percent
2010
2007
2004
2001
1998
1995
1992
1989
1986
50,000
Source: Internal Revenue Service
Note: An “income percentile threshold” is the lowest amount earned by a household in that
percentile. Income is “adjusted gross income,” which is income minus certain deductions.
Amounts are in current dollars.
same opportunities for movement, then differences
in income, wealth, or education must at least partially
reflect deliberate choices and not market structure. This
is not a setting in which many people would find efforts
to alter outcomes via policy compelling. In contrast, to
the extent that inequality continues across generations
because people do not have the same chances, then
inequality and immobility can be partially chalked up
to market structure. From a normative standpoint,
there thus might be support for policy interventions
that seek to equalize opportunities, rather than those
that would equalize outcomes.
One such intervention is greater investment in early
education. High-quality early-childhood education
equips children with the skills they need to succeed at
each subsequent stage of life, yet in the United States,
access to such education appears to strongly depend on
parents’ income. Children of poor parents are thus at a
disadvantage from the very beginning—a disadvantage
6
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
from which it is very difficult to recover. But these children
are not the only ones who are affected; all else equal,
a more skilled workforce increases the productivity of
society as a whole. Enhancing early education opportunities for the initially disadvantaged could therefore lead
to better economic outcomes for everyone.
This essay will review both recent and longer-run
features of U.S. economic mobility, with a focus on
how those trends affect the interpretation of data
on income inequality. It then will discuss some of the
challenges and choices facing policymakers seeking
to alter observed outcomes.
Inequality in the United States
By nearly any measure, income inequality in the
United States is increasing.1 In particular, today’s rich
are both richer than their counterparts in the past
and richer relative to those around them. In 1979, the
Research suggests that early childhood
education is the most cost-effective way to
reduce the opportunity gap.
top 1 percent of households took home 7.4 percent of
total after-tax income in the United States. By 2007,
the share had more than doubled to 16.7 percent
(Congressional Budget Office 2011).2 At the same
time, the share of income earned by households
at all levels of the remaining distribution stayed
flat or declined. Those in the middle three quintiles
(fifths), for example, saw their share decrease from
51 percent to 43.9 percent. The picture looks the
same for pretax income; the share accruing to the
top 1 percent rose from 8.9 percent to 18.7 percent
(Congressional Budget Office 2011).3 These changes
are a result both of increasing concentration of all
types of income at the top of the distribution and a
shift in the composition of income toward business
income and capital gains (Congressional Budget
Office 2011). This compositional change also makes
incomes at the top of the distribution more volatile,
but the trend is clearly one of growing inequality.
(See Figure 2.)
Other research shows similar trends. Thomas Piketty
and Emmanuel Saez (2003) find that after remaining flat throughout the 1950s and 1960s, the share
of pretax income earned by the top 10 percent of
households increased from 31.5 percent in 1970 to
41.4 percent in 1998.4 As in the CBO’s analysis, this
increase was largely driven by those at the very top
of the distribution. While the income share for those
in the 90th through 99th percentiles increased from
23.7 percent to 26.9 percent, the share for those in the
very top percentile nearly doubled, from 7.8 percent
to 14.6 percent.5
The trend continued after the 2007–09 recession.
Although average real income for the top 1 percent
fell about three times more than for the remaining 99
percent, the decline was almost entirely due to the stock
market crash. As markets recovered in 2010, incomes
for the top 1 percent increased 11.6 percent, compared
to only 0.2 percent for all other households (Saez 2013).
7
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
Figure 2: Income Distribution by Quintiles
The top quintile (fifth) of households account for about half of after-tax income
SHARE OF AFTER-TAX INCOME
60
18
16
40
12
Top 1 Percent
(right scale) 10
30
8
Fourth Quintile
20
6
Third Quintile
Second Quintile
10
2009
2007
2005
2003
2001
1999
1997
1995
1993
1991
1989
2
1987
1985
1983
1979
The top 1 percent of
households account
for much of the gap
between the fourth
and top quintiles.
4
Bottom Quintile
1981
Percent
14
Top Quintile
Percent
50
Source: Congressional Budget Office
Note: Quintiles are displayed on the left scale; the top 1 percent is displayed on the right scale.
After-tax income is defined as market income (labor income, business income, capital gains,
capital income, and other income) net of transfer payments and taxes.
Income shares for the 90th–99th percentiles and the
top 1 percent continued to increase, to 29.1 percent
and 17.4 percent, respectively, in 2011 (Piketty and Saez
2003, updated data).
These data have garnered a great deal of attention
from economists, policymakers, and the public, but
do they shed light on what is actually happening to
individuals or households?
Mobility: A Central Force
Behind Inequality
An observation of inequality at any point in time is
only a snapshot; it does not shed light on how that
snapshot developed. For example, imagine three different worlds: In the first world, the first inhabitants flip
coins to determine not only their income, but also the
income of all future generations; each descendant earns
either $1,000 or $100,000 per year, depending on his
8
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
ancestor’s original coin toss. In the second world, the
members of each new generation flip coins, but they
do so just once at birth to determine whether they will
earn $1,000 or $100,000 per year during their lifetimes.
In the third world, individuals get to flip a coin each
year to determine their income for that year.
The people in these worlds face very different lifetime
risks. The first world, which is akin to a caste system,
is very risky from the perspective of the first ancestor,
who is determining outcomes for an entire dynasty.
The second world also is risky since the die is cast for
one person’s entire life, but each of her descendants
gets a chance to flip the coin, making it unlikely that
bad luck will persist across many generations. The
third environment is the least risky since it is very
unlikely that an individual’s average annual income
over his lifetime would be significantly different than
$50,500, the average annual income he can expect
over many years.
Despite these differences, snapshots of these economies in any given year look the same. In each, about
half the population earns $1,000 per year, while the
other half earns $100,000. Clearly, then, inequality
data alone do not reveal the underlying prospects of
individuals. For this, one must study economic mobility.
Trends in Economic Mobility
Economists and policymakers generally are interested
in two types of mobility: intragenerational and intergenerational. Intragenerational mobility describes
how a given person’s economic status changes over
the course of his lifetime. Intergenerational mobility
reflects the degree to which a person’s economic
status as an adult differs from that of her parents
or ancestors. Status is usually measured by earnings (wage income), income (all sources of income,
including wages), or less frequently wealth (the value
of assets minus liabilities). Most research focuses on
relative intra- and intergenerational mobility, or how a
person’s status changes in comparison to others. But
it is also important to recognize that a person might
experience absolute mobility even in the absence of
relative mobility. She might occupy the same place in
the earnings distribution as her parents, remaining in
the same position relative to the rest of society, but
still have a higher standard of living than her parents
did, depending on the rate of economic growth.6
Intragenerational Earnings Mobility
Does the top of the income distribution comprise the
same people year in and year out, or do individuals
flow in and out of the highest percentiles over their
lifetimes? If intragenerational mobility is high, then any
snapshot of inequality will overstate the actual longterm inequality among individuals. For example, it is
possible that the large gap in recent years between
those in the top percentile and the rest of the distribution reflects an increase in the variation of annual
earnings due to stock options and large bonuses. If
that were the case, short-term inequality might be
high, but long-term inequality could be much lower,
reflecting high mobility.
In addition, in most modern societies, there is a clear
life-cycle pattern to earnings and income. Imagine
an extreme case where half the population earns
$1,000 during the first half of their lives and $100,000
during the second half, while the other half of the
population earns $100,000 early in life and $1,000
later. Income inequality would be high at a point in
time, but everybody has the same lifetime income.
Assuming that individuals could save and borrow to
smooth their consumption over time, the snapshot
of income inequality might not accurately reflect
people’s well-being since consumption inequality—a
truer, and harder to measure, barometer—would be
relatively low.
Anthony Shorrocks (1978) formalized these ideas by
developing an index in which mobility is defined as the
extent to which income inequality decreases over a
given timeframe. Wojciech Kopczuk, Emmanuel Saez,
and Jae Song (2010) calculate Shorrocks indices comparing inequality in annual earnings and in earnings
averaged over five years for workers between 1937 and
2004. They find that short-term (five-year) mobility
has not changed over the period, which implies that
greater volatility of short-term earnings is not the
source of observed higher inequality. Instead, higher
inequality is likely the result of increased variation
in lifetime earnings, including higher earnings at the
top of the distribution. The authors conclude that
mobility has not been sufficient to offset the rise in
inequality, and thus that short-term inequality likely
reflects lifetime inequality.
Kopczuk, Saez, and Song (2010) also find that longterm income mobility, from the beginning to the end
of working life, actually increased significantly for all
workers between 1942 and 1999. There is significant
9
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
heterogeneity among groups of workers, however.
Although on average men are more upwardly mobile
than women, men’s mobility was stable or declining
during the sample period. Women’s mobility, however,
has increased greatly since the 1960s, as more women
have moved into higher-paying professions. Thus, the
increase in mobility for all workers has been driven by
the labor market experiences of women.
Heterogeneity in intragenerational mobility also is
apparent across the income distribution. Gerald Auten,
Geoffrey Gee, and Nicholas Turner (2013) find that
about 75 percent of taxpayers aged 35–40 who were
in the second, third, or fourth quintile in 1987 were in a
different quintile in 2007. (About 60 percent of those
who changed position moved up or down a single
quintile.) But they find greater persistence at the top
and bottom of the distribution: 43 percent of taxpayers in the bottom quintile were still there 20 years
later, and 46 percent of taxpayers in the top quintile
maintained their positions. The authors also find that
the very top earners tended to remain top earners:
From 1992 through 2006, between 60 percent and
70 percent of the top 1 percent in a given year were
in the top 1 percent in the following year.
Intergenerational Mobility
A commonly used measure of intergenerational mobility is the intergenerational elasticity of earnings (IGE).
The IGE describes in percentage terms how much
of the difference between the earnings of families
in one generation persists into the next generation,
typically by comparing the correlation of the earnings
of fathers and sons. For example, an IGE of 0.5 means
that a 10 percent difference between the income of
two fathers translates into a 5 percent difference in the
income of their sons. The smaller the IGE, the greater
the amount of mobility.
Important early studies of the United States and other
developed countries found a high degree of mobility,
10
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
with an IGE of 0.2 or less (Becker and Tomes 1986).
Later research, however, found that data used in this
work featured biases that would lead to artificially low
measurements of the true level of earnings persistence.
(See Stokey [1996] for a review of this research.)
New and better data suggest that mobility in the
United States has been historically lower than initial
estimates implied, and that it has declined even
further in recent decades. Daniel Aaronson and
Bhashkar Mazumder (2008) construct a time series
of intergenerational elasticity from 1950 to 2000.
They find that mobility increased between 1950 and
1980—the IGE decreased from 0.40 to 0.32—but
decreased significantly during the 1980s and 1990s,
with the IGE reaching 0.58 by 2000.
Although exact international comparisons are not
possible, most research suggests that people in the
United States are somewhat less mobile than people
in Canada, Denmark, Finland, and Norway, where the
IGE is about 0.15 to 0.2. In Germany and Switzerland,
the IGE is about 0.3, and people in the United Kingdom
and France also are relatively immobile, with IGEs of
about 0.4 to 0.5 (Corak 2006).
While the IGE is a widely used statistic in work on intergenerational mobility, it only reflects average mobility
across the entire distribution of individuals; it does not
reveal anything about the direction of mobility or how
it varies across different groups. To learn more about
such mobility, Mazumder (2008) calculates transition
rates, the likelihood of moving from one point in the
distribution to another, across generations. He finds
that, as with intragenerational measures, the amount
of mobility varies significantly according to income.
For example, there is a great deal of “stickiness” at
the top and bottom of the distribution; people whose
parents are in the bottom quintile of income are more
likely to be in the bottom quintile themselves, and
those whose parents are in the top quintile are likely to
remain there. More than 60 percent of children whose
parents are in the bottom quintile will end up in the
bottom or second quintile, compared to 23.3 percent
of those whose parents are in the top quintile. Only
7.4 percent of people who reach the top quintile are
from families in the bottom quintile. (See Figure 3.)
There also are stark differences between black people
and white people and between men and women.
Whites appear to be more upwardly mobile and less
downwardly mobile than blacks. Mazumder (2008)
finds that about 24.9 percent of whites remain in the
bottom quintile, compared to 43.7 percent of blacks.
And 38.9 percent of whites remain in the top quintile,
compared to 21.3 percent of blacks. In addition, more
than twice as many whites as blacks experience the
“rags-to-riches” scenario of moving from the bottom
quintile to the top quintile, 10.6 percent compared to
4.1 percent. Mazumder also finds a large gender gap.
While 40.5 percent of women from families in the
lowest quintile remain there, only 27.2 percent of men
do. Conversely, 43.0 percent of men from families in
the top quintile remain in that quintile, compared to
31.9 percent of women. Men are thus more upwardly
mobile and less downwardly mobile than women. The
gender gap is trumped by the race gap, however: Both
black men and black women tend to be the most likely
Forty-three percent of men from
families in the top quintile remain there,
compared to 31.9 percent of women.
11
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
Figure 3: Intergenerational Income Quintile Transition Rates
Income Quintiles as Adults
Top
100%
7.4
90
Fourth
15.2
Third
Second
17.8
21.7
12.5
37.8
80
19.3
19.6
70
Transition Rate
Bottom
22.3
24
60
20.4
50
22
21.6
26.9
21.5
40
23.6
30
16.8
20.2
20
The majority of
adolescents from the
top or bottom quintiles
remained in the same
quintile or adjacent
quintile as adults.
16.9
12.4
33.5
21.6
10
Bottom
Second
18
15.9
Third
Fourth
10.9
Top
Families’ Income Quintiles
Source: Mazumder (2008)
Note: The figure shows what percentages of adolescents from families in a given income quintile
remained in that quintile or transitioned to a different quintile as adults. For example, 33.5
percent of adolescents from families in the bottom quintile remained in the bottom quintile,
while 26.9 percent moved to the second quintile. Income data were gathered from 1979 through
1980 and again from 1997 through 2003.
to remain in the bottom quintile and the most likely to
fall out of the top quintile.7
Mobility of Immigrants
For centuries, the American dream has drawn immigrants to the United States, from the waves of German
and Irish immigrants in the late 1800s to the nearly 12
million Mexican immigrants who arrived during the
past four decades.8 But how likely is it that the dream
becomes a reality?
Decennial census data indicate that immigrants’ earnings increase rapidly after they arrive in the United
States; the earnings gap between them and their
native-born peers appears to shrink substantially over
12
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
time. Comparing natives and immigrants with similar
work experience, Darren Lubotsky (2007) finds that the
positive earnings gap between natives and the cohort
of immigrants who came to the United States between
1965 and 1969 fell from 38 percent in the 1970 Census
to 16 percent in the 1980 Census, and vanished by the
1990 Census. The gap between natives and immigrants
who arrived in the late 1980s fell from 55 percent to 36
percent between the 1990 and 2000 censuses. This
mobility might be spurious, however. Up to one-third
of immigrants eventually return to their home countries; if these immigrants tend to be those with lower
earnings, then the apparent earnings growth actually
reflects fewer low earners in the data pool. Lubotsky
(2007) corrects for this “selective out-migration” by
studying longitudinal rather than cross-sectional data,
and finds that earnings growth is significantly lower.
In the cross-sectional data, immigrants’ relative earnings increase 20 percent during their first decade in
the United States and an additional 10 percent to 20
percent in each following decade. In the longitudinal
data, however, immigrants’ earnings grow between
12 percent and 15 percent during their first 15 years in
the country and then stagnate.
The mobility of the second generation also appears
to be decreasing. Throughout the 20th century, the
children of immigrants not only earned more than their
parents, but they also earned more on average than
the rest of the non-immigrant population, perhaps
reflecting some of the selection effects Lubotsky
(2007) observed. But that advantage is shrinking. In
1940, the second generation earned 17.8 percent more
than non-immigrants on average. In 1970, the difference was 14.6 percent, and by 2000, the difference
had fallen to 6.3 percent (Borjas 2006). The reason
might be a shift in the composition of immigrants.
There has long been significant heterogeneity in
earnings among immigrant groups, and in recent
times, immigrants from developed countries tend
to earn more than those from developing countries.
Immigrants from Germany earned 24.9 percent more
than non-immigrants in 1970 and their children earned
19.5 percent more in 2000, for example, while those
from Mexico earned 31.6 percent less in 1970 and their
children earned 14.6 percent less in 2000 (Borjas
2006).9 While wages in the second generation tend
to regress toward the mean, overall earnings show
significant persistence into the second generation.
Borjas (2006) finds that across all immigrant groups,
the intergenerational elasticity over the period 1970
to 2000 is 0.43. As the composition of immigrants
increasingly shifts toward people from less-developed
countries, who tend to have lower skills and levels of
education, the wage gap is likely to persist through
successive generations of immigrants (Haskins 2008).10
Irrespective of how quickly immigrants’ earnings
approach the earnings of natives, many immigrants still
improve their economic status significantly by immigrating to the United States. In this sense, the move to the
United States is a powerful form of economic mobility,
and the United States’ absorption of both legal and
illegal immigrants makes it an engine of global mobility.
This last point must be part of any meaningful assessment of the mobility offered by a society. Even a
calcified society, in which intergenerational or intragenerational mobility of natives is low, may be a source
of mobility for the world’s residents via its openness
to immigrants. Conversely, societies that promote
intergenerational mobility of natives through intensive
early intervention and generous social safety nets but
limit entry of immigrants—perhaps out of fear that they
will exploit the generous safety nets—might hinder
equality of opportunity in a global sense.11
What Generates Persistence?
The preceding discussion has highlighted empirical
findings on the persistence of economic outcomes
both within and across generations. But these findings
do not explain why persistence across generations
exists in the first place or why it might have increased.
As Aaronson and Mazumder (2008) note, intergenerational elasticities do not reflect causality. Instead,
measures like the IGE are simply omnibus measures
of everything correlated with parents’ income and
children’s future earnings—factors ranging from the
neighborhood where a child grew up to the availability
of health care, among many others.
Intuitively, parents’ decisions to invest in developing
their children’s skills, or “human capital,” are important.
Their willingness to make such investments stems in
large part from altruistic concern for their children.12 One
model that incorporates this dynamic was created by
Gary Solon (2004). He relates this investment decision
13
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
In the 20th century, the children of
immigrants earned more on average
than their parents and more than the
rest of the non-immigrant population.
to the rate of return to human capital and to the progressivity of public investment in children’s human
capital, such as government provision of education and
health care. Solon’s model suggests several things: that
higher-income parents invest more in their children’s
human capital, that more progressive public investment
in children’s human capital partially crowds out parents’
investment, and that parents are likely to invest more
when the returns to human capital increase. The model
predicts that intergenerational mobility will decrease
during a period of increasing returns to human capital
because rich parents are able to invest more than poor
parents, and that mobility will increase during a period
of more progressive public investment.
Recent trends in intergenerational mobility do correspond to Solon’s predictions (Mazumder 2012).
The returns to college education dropped during the
1940s, remained steady for several decades, and then
began rising around 1980. These turning points in the
14
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
returns to college education match the turning points
in intergenerational elasticity observed in Aaronson
and Mazumder (2008), as well as in other studies of
mobility trends.
In Solon’s (2004) model, the degree of progressivity of
public education is exogenous—that is, determined outside the model. Andrea Ichino, Loukas Karabarbounis,
and Enrico Moretti (2011) develop a model in which
the degree of progressivity is the outcome of sociopolitical forces. In their model, public education is an
insurance system that increases the future income of
children without much innate talent at the expense
of the future income of children with high innate
talent. Public education thus increases mobility. But
currently rich dynasties prefer low mobility for their
descendants (as will be discussed in more detail in the
following section), so in countries where rich dynasties
are more politically active, spending on public education will be lower.
THE ROLE OF CHOICE
Inequality and immobility partially reflect deliberate choices related to the fact that people differ in their
tolerance for risk or in their willingness to defer gratification (what economists call “time discounting”). But
these differences cannot be directly observed. Instead, economists must make inferences based on actual
outcomes, such as occupational choice, savings, and consumption.
Risk tolerance has a large impact on occupational choice, and thus on income and wealth. Beginning with
Frank Knight’s Risk, Uncertainty, and Profit (1921) and continuing in modern work since Richard Kihlstrom and
Jean-Jacques Laffont (1979), economists have modeled entrepreneurs as less risk averse than other people
and therefore more likely to undertake high-risk/high-return enterprises. To the extent that people genuinely
vary in risk aversion, this model suggests that the rich and the poor disproportionately will be those with
high risk tolerance, while those in the middle will be more risk averse. This is consistent with data that show a
disproportionate number of self-employed people at both ends of the earnings and wealth spectrums. They
also figure more prominently among households in financial distress (Sullivan, Warren, and Westbrook 2000).
Additional evidence for the role of risk tolerance in personal economic outcomes comes from Sam SchulhoferWohl (2011), who finds that risk-tolerant workers tend to have jobs more exposed to economy-wide or “aggregate” risk. Movements in these workers’ incomes thus tend to be more volatile even when they have insured
themselves against individual-level, or “idiosyncratic,” risks, such as job loss or illness. As a result, volatility
in their consumption of goods and services is not necessarily evidence of poor insurance possibilities in the
marketplace. Indeed, Schulhofer-Wohl (2012) finds that after correcting for this bias, U.S. households do not
appear to be bearing any significant uninsurable risk. (A variety of other research, however, has found that
certain types of shocks, such as a long-term disability, are clearly not fully insured.)
Observed inequality also might reflect different preferences for consumption in the present versus the future.
Per Krusell and Anthony Smith (1998) show, for example, that a model that includes variation in “impatience,”
or the willingness of households to borrow against future earnings, successfully matches observed wealth
inequality in the U.S. population. Emily Lawrance (1991) and Marco Cagetti (2003) also find that data on
consumption and wealth suggest the presence of significant differences in preferences, especially in riskaversion and time discounting. They find that less-skilled and less-wealthy individuals generally are less
patient—meaning they place a higher value on current versus future consumption—than their more-skilled
and wealthier counterparts. More recently, Lutz Hendricks (2007) has measured the extent of differences in
households’ discount factor by noticing that households vary a great deal in their wealth even though they
have and can expect to have very similar lifetime incomes.
Taken as a whole, economists’ work suggests that many of the observed differences in the way households
make decisions can be understood as arising from differences in risk tolerance or time discounting. A caveat,
however, is that a variety of difficult-to-model environmental forces might play a large role in generating
these differences. In a society with low life expectancy or a high violent crime rate, for example, individuals
might not be “choosing” to be impatient so much as making a rational decision to value current over future
consumption. Likewise, not attending college might indicate an individual with a high discount factor who
chose not to invest in K-12 education—or it might indicate a person facing strong institutional barriers to
attending college. It is important to keep such environmental factors in mind when interpreting any model
that includes heterogeneity in preferences.
15
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
In the United States, spending on public education
mostly begins with kindergarten. But children face
differences even before they begin school that may
determine their future success. Mazumder (2008) finds
that educational attainment alone is not enough to
explain different mobility rates among black and white
children. Black and white people who have completed
the same number of years of school still have different
intergenerational mobility rates, particularly at the level
of high school completion and below. Other research
also has found that educational attainment can explain
less than half of the intergenerational transmission of
earnings (Bowles, Gintis, and Groves 2008).
What this research implies is that human capital embodies more than the number of years spent in school. For
example, adolescents who score higher on the Armed
Forces Qualifying Test (AFQT) are more likely to move
out of the bottom income quintile, and differences in
AFQT scores can explain nearly all of the black/white
mobility gap (Mazumder 2008).13 These test scores,
however, capture much more than innate intelligence or
academic achievment; non-cognitive skills such as work
ethic, the ability to follow instructions, motivation, and
patience also are essential to success on such standardized tests (Bowles, Gintis, and Groves 2008; Heckman
2008). In fact, these non-cognitive skills may be just
as important as cognitive skills in determining future
success in the labor market. For example, the General
Educational Development (GED) credential is supposed
to demonstrate cognitive equivalence between people
who have graduated from high school and people who
have dropped out and taken the GED exam instead. But
GED holders have much poorer labor market outcomes
than high school graduates despite obtaining equivalent
knowledge. The reason, James Heckman and other
economists have concluded, is that many students
who earn a GED lack the non-cognitive skills that would
have enabled them to complete high school—the same
skills that would help them succeed in the labor market
(Heckman, Humphries, and Mader 2010).
16
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
Recognizing the importance of non-cognitive skills
begs an important question: How do children acquire
these skills? A consensus now exists that the foundation is laid very early in life, even from infancy. Skill
development is hierarchical; the early mastery of
basic emotional, social, and other non-cognitive skills
makes it easier to learn more complex cognitive skills
throughout life. And children who fall behind early
have difficulty catching up. Gaps in cognitive skills
that are important for adult outcomes are present
as early as age 5 and tend to persist into adulthood
(Heckman 2008).
The data suggest that poor and minority children
are much more likely to fall behind. A recent report
from the Brookings Institution (Sawhill, Winship, and
Grannis 2012) examines the likelihood of achieving
certain social and economic milestones on the path
to the middle class, defined in the report as having
a family income at least 300 percent of the poverty
level, or about $70,000 for a married couple with two
children. Only 48 percent of children from families
in the bottom income quintile are ready for school
at age 5, compared to 78 percent of children from
families in the top quintile.14 There also is a large
disparity in early childhood outcomes according to
race. Sixty-eight percent of white children are ready
for school at age 5, versus only 56 percent of black
children and 61 percent of Hispanic children. The
gap between white and black widens throughout
the lifespan. By age 11, 73 percent of white children
versus 52 percent of black children have basic reading
and math skills. By age 29, only 33 percent of black
people have successfully transitioned to adulthood
(defined by the authors as living independently and
having either a college degree or a family income
at least 250 percent of the poverty level), while
68 percent of white people reach this milestone.
Hispanic people fare somewhat better; 66 percent
achieve the age-11 milestone, and 47 percent reach
the age-29 milestone.
Educational attainment alone is not
enough to explain different mobility
rates for black people and white people.
Challenges for Policymakers
What is the role for public policy, if any, in addressing
economic inequality and mobility? Answering this
question requires asking several others: What would
policy try to achieve, and in particular, whose wellbeing would it attempt to enhance? Would the goal
be to improve opportunities for current cohorts or for
future generations? Would policy treat individuals at
different moments in time as discrete units, irrespective
of their ancestors, or would it emphasize dynasties
by taking into account how family members invest
in descendants?
From a policymaker’s point of view, mobility might
be inadequate as a measure of what a good society
should provide its members. First of all, there is a
tradeoff between mobility and predictability. Recall
the imaginary world resembling a caste system
described earlier. This setting is utterly immobile
and risky for each dynasty’s first member. But it is
perfectly safe for the members of each successive
generation since income is completely stable. In fact,
for a person whose ancestor flipped the $100,000 coin,
this world is not only safe, but also quite comfortable.
On the macro level, it is possible that the costs of
large fluctuations and risky income patterns outweigh
the benefits of high mobility and reduced inequality.
Peter Gottschalk and Enrico Spolaore (2002) study
a model in which there are large welfare gains from
greater mobility if aversion to inequality is the only
consideration. But if aversion to income fluctuations
is considered, those gains disappear. Of course, this
might not be of great consolation to a person whose
ancestor flipped the $1,000 coin.
In addition, a world in which mobility is high is one
where parents are of little consequence, despite
their desire or ability to position their children and
grandchildren for future success. Few parents would
want to live in a world where their investments in their
17
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
Shocks to earnings, such as job losses,
are essential to explaining earnings
dispersion in the economy.
children have no influence beyond their lifetimes. The
flip side is that descendants of people who were not
altruistic or who made poor decisions would not be
as constrained by their ancestors’ actions.
Viewed in this light, what most people might agree on
is trying to promote individual productivity while limiting downward mobility. Broadly speaking, the former
goal involves ensuring preparedness at labor market
entry, while the latter involves insuring households
against low innate abilities, poor health, or job loss.
Knowing the extent to which these forces matter is
crucial for policy interventions to be effective. For
example, if workers were similarly prepared at the
time of entry into the labor market, and shocks in
working life were important, the question would be
how, if at all, to better insure workers, and not how to
alter educational investment decisions. Conversely,
if preparedness differed and shocks during working
life were unimportant, further insuring workers would
18
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
yield little benefit. Instead, changes to the educational
system would be more effective.
Both factors are important, according to a recent line
of work exemplified by Mark Huggett, Gustavo Ventura,
and Amir Yaron (2011). They find that about 60 percent
of the observed disparity in lifetime earnings is due to
individual differences that exist before people enter
the labor market, and the remainder is due to shocks
that buffet them as they work, such as job losses. Their
research stresses that the observed evolution of earnings inequality over lifetimes is consistent with a simple
setting in which all workers accumulate skills through
experience and effort, but do so at substantially different rates that reflect their initial “learning” ability.
At the same time, their estimates clearly indicate that
a substantial portion of inequality is generated during
working life. This suggests that shocks to earnings are
essential to a successful theory of earnings dispersion
in the economy.
A critical point here is that the disparity in learning
ability likely arises not only from differences in innate
ability, but also from forces such as the quality of K-12
education and parental and cultural influences. These
forces are very different for children from poor versus
rich families—a dynamic that is magnified by a labor
market that demands increasing levels of skill.
Investing in Human Capital
For most people—all but a lucky few—labor is what
they can sell to generate income. They can increase
the value of their labor by acquiring greater skills, but
the value of their labor is only partially under their
control. It also depends on the supply and demand
for their skills in the marketplace.
The industrial revolution, for example, created factories
that made workers more productive and more valuable
without substantially increasing their skills. But the
information revolution has created a marketplace that
rewards personally acquired skills, such as computer
programming or mathematical analysis. In this new
environment, an individual’s innate ability and early
life education become critical because they largely
determine the levels of skills each person can develop
to “rent” to the marketplace.
Given the large earnings gap between workers with
and without college degrees, many policies aim to
increase college access, for example by increasing
federal subsidies for student loans. But it’s not clear
that college is the best focus for policymakers. The
observed disparity between high school and college
graduates applies to students who have graduated
from college already; those students who have not
yet enrolled might not necessarily receive the same
benefit, perhaps because they are not as well prepared.
For example, Lutz Hendricks and Oksana Leukhina
(2012) find in preliminary work that about 70 percent
of the lifetime earnings gap between high school and
college graduates results from ability selection rather
than from attaining the college degree per se. In other
words, the college graduates were likely to be better
earners even before entering college.
Intervening well before college could yield much higher
returns. As noted above, the skills learned early in life
prepare children to obtain more complex skills later in
life. Heckman and many other researchers have found
that the return on a dollar invested in human capital is
highest when the investment occurs at age 3, and that
children who receive high quality early education fare
much better on a variety of socioeconomic measures
(Heckman 2008).
The most cost-effective policy for increasing equality of
opportunity is thus likely to be one that shifts funding
away from universal college subsidies and toward early
childhood interventions. Elizabeth Caucutt and Krishna
Kumar (2003) find that a large increase in college subsidies with the goal of reducing the “enrollment gap”
leads to very inefficient use of education resources, with
little or no welfare gain, because more poorly prepared
students enroll and the dropout rate increases. In a model
of human capital transmission in which parents invest in
their children, Diego Restuccia and Carlos Urrutia (2004)
find that subsidies for investment in early education
are much more effective at mitigating persistence in
earnings than subsidies for college.
Investments in early childhood education can be viewed
as a form of insurance against the risk of being born
to poor parents, among other things. And while the
public provision of such insurance could yield a big
“bang for the buck” by enabling current generations
to invest more in the education of future generations,
one must also acknowledge the potential for moral
hazard. A public system that equalizes the educational
opportunities (or far more ambitiously, the home environments) of poor and rich children could reduce the
incentives of all parents to invest in children.15
19
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
Greater public investment in early childhood education
cannot replace the advantages that some parents are
able to bestow upon their children, nor can it guarantee that all children will grow up to be prosperous.
But such investments could give more children the
necessary foundation for future acquisition of skills,
and ensure that large amounts of human capital are
not foregone simply because many children are born
to poor families. This foregone human capital is a loss
not only for the child, but also for society as a whole.
According to an influential line of research, long-run
economic growth depends on the amount of human
capital in a society.16 Unlike physical capital, which
exhibits decreasing returns to scale, human capital
might well exhibit increasing returns. Knowledge
leads to new ideas and new technologies, which lead
to higher productivity, thus raising per capita income
and living standards for society as a whole.
As this essay has discussed, economic inequality has
increased significantly in the United States in recent
years. At the same time, data suggest that economic
mobility also has decreased, particularly for those born
at the top and the bottom of the income distribution.
Many factors contribute to the attainment and persistence of economic status, including innate ability,
preferences for present versus future rewards, aversion
20
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
to risk, and quite a bit of luck. But for nearly all people,
advancement depends critically on opportunities to
obtain human capital—and those opportunities are not
the same for children born to poor versus rich families. Policies that aim to equalize these opportunities,
particularly very early in life, appear to yield a very
high return on investment, although much remains to
be learned about the feasibility of implementing such
interventions on a large scale. Nonetheless, such efforts
have the potential to help the United States achieve a
more inclusive prosperity. n
Kartik Athreya is the group vice president for microeconomics and research communications, and Jessie Romero
is an economics writer in the Research Department at the
Federal Reserve Bank of Richmond. The authors would
like to thank Huberto Ennis, Arantxa Jarque, Marianna
Kudlyak, Karl Rhodes, and Aaron Steelman for valuable
discussions and insights.
The views expressed are those of the authors and not
necessarily those of the Federal Reserve Bank of Richmond
or the Federal Reserve System.
E ndnotes
1. Economists also study consumption inequality, or
differences in the amounts of goods and services
that households purchase. Consumption inequality might differ from income inequality because
of savings, taxes, or in-kind benefits such as food
stamps. Some recent research suggests consumption
inequality is much less pronounced than income
inequality (e.g., Meyer and Sullivan [2013]), although
other research finds that the trends in income and
consumption inequality are very similar (e.g., Aguiar
and Bils [2011]).
2. The CBO defines after-tax income as market income
(labor income, business income, capital gains,
capital income, and other income) plus government transfers (such as Social Security payments,
unemployment benefits, or in-kind transfers such
as food stamps) minus taxes paid.
3. Data are from the supplemental data tables posted
at www.cbo.gov/publication/43373.
4. In Piketty and Saez (2003), the unit of analysis is
a tax unit, defined as two married people living
together (with or without dependents) or a single
adult (with or without dependents). Their income
measure excludes capital gains.
5. Updated data are available at elsa.berkeley.edu/~saez/
TabFig2011prel.xls.
6. For example, see Easterlin (2000).
7. Isaacs (2008) finds similar differences in black and
white mobility.
9. Because the flow of immigrants from Mexico has
been substantially greater than the flow from developed countries, the average wage of first-generation
immigrants is still lower than the average wage of
their native-born peers.
10. Immigrant mobility matters not only for the prospects of the immigrants themselves, but also for
measured inequality in society as a whole. Imagine a
room in which everyone is six feet tall. If a group of
shorter people enter the room, measured inequality
in height will increase. In the context of immigration,
the arrival of a group with wealth, skills, or education significantly different from those of natives can
mechanically increase inequality at a point in time.
11. See, for example, Pritchett (2006).
12. For a thorough treatment, see Mulligan (1997).
13. The AFQT is administered by the military to determine qualification for enlistment. AFQT scores
have been widely used by economists as a measure
of pre-labor market skills.
14. The authors define “school-ready” as having acceptable pre-reading and math skills and behavior that
is generally school-appropriate.
15. See Chang and Kim (2012) and Seshadri and Yuki
(2004) for more on the “price of egalitarianism.”
16. Influential papers on “endogenous growth theory”
include Romer (1986) and Lucas (1988).
8. The number includes undocumented immigrants.
Since the 2007–09 recession, net migration from
Mexico has fallen to virtually zero. Between 2007
and 2011, the number of undocumented Mexican
immigrants in the United States declined by about 1
million (Passel, Cohn, and Gonzalez-Barrera 2012).
21
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
W or k s C ited
Aaronson, Daniel, and Bhashkar Mazumder. Winter 2008.
“Intergenerational Economic Mobility in the United States,
1940 to 2000.” Journal of Human Resources 43 (1): 139–172.
Easterlin, Richard A. Winter 2000. “The Worldwide Standard
of Living since 1800.” Journal of Economic Perspectives 14
(1): 7–26.
Aguiar, Mark A., and Mark Bils. February 2011. “Has
Consumption Inequality Mirrored Income Inequality?”
National Bureau of Economic Research Working Paper
No. 16807.
Gottschalk, Peter, and Enrico Spolaore. 2002. “On the
Evaluation of Economic Mobility.” Review of Economic
Studies 69 (1): 191–208.
Auten, Gerald, Geoffrey Gee, and Nicholas Turner. January
4, 2013. “Income Inequality, Mobility and Turnover at
the Top in the United States, 1987–2010.” Paper presented at the Allied Social Science Associations Annual
Meeting, San Diego.
Becker, Gary S., and Nigel Tomes. July 1986. “Human
Capital and the Rise and Fall of Families.” Journal of Labor
Economics 4 (3) Part 2: S1–S39.
Borjas, George J. Fall 2006. “Making It in America: Social
Mobility in the Immigrant Population.” Future of Children
16 (2): 55–71.
Bowles, Samuel, Herbert Gintis, and Melissa Osborne
Groves. 2008. “Intergenerational Inequality Matters.”
In Unequal Chances, edited by Samuel Bowles, Herbert
Gintis and Melissa Osborne Groves, 1-22. Princeton,
N.J.: Princeton University Press.
Cagetti, Marco. July 2003. “Wealth Accumulation Over the
Life Cycle and Precautionary Savings,” Journal of Business
and Economic Statistics. 21 (3): 339–353.
Caucutt, Elizabeth M., and Krishna B. Kumar. 2003. “Higher
Education Subsidies and Heterogeneity: A Dynamic
Analysis.” Journal of Economic Dynamics and Control 27:
1459–1502.
Chang, Yongsung, and Sun-Bin Kim. January 2012. “The Price
of Egalitarianism.” B.E. Journal of Macroeconomics 11 (1).
Congressional Budget Office. July 2012. “The Distribution of
Household Income and Federal Taxes, 2008 and 2009.”
Congressional Budget Office. October 2011. “Trends in
the Distribution of Household Income Between 1979
and 2007.”
Corak, Miles. 2006. “Do Poor Children Become Poor
Adults? Lessons from a Cross-Country Comparison of
Generational Earnings Mobility.” In Research on Economic
Inequality, Vol. 13, edited by John Creedy and Guyonne
Kalb, 143–188. Bingley, U.K.: Emerald Group Publishing.
22
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT
Haskins, Ron. 2008. “Immigration: Wages, Education, and
Mobility.” In Getting Ahead or Losing Ground: Economic
Mobility in America, edited by Julia B. Isaacs, Isabel V.
Sawhill, and Ron Haskins, 81–90. Washington, D.C.: The
Pew Charitable Trusts.
Heckman, James J. July 2008. “Schools, Skills, and Synapses.”
Economic Inquiry 46 (3): 289–324.
Heckman, James J., Lance J. Lochner, and Petra E. Todd.
February 2008. “Earnings Functions and Rates of Return.”
National Bureau of Economic Research Working Paper
No. 13780.
Heckman, James J., John Eric Humphries, and Nicholas
S. Mader. June 2010. “The GED.” National Bureau of
Economic Research Working Paper No. 16064.
Hendricks, Lutz. September 2007. “How Important Is
Discount Rate Heterogeneity for Wealth Inequality?”
Journal of Economic Dynamics & Control 31(9): 3042–3068.
Hendricks, Lutz, and Oksana Leukhina. May 2012. “The
Return to College: Selection Bias and Dropout Risk.”
Manuscript, University of North Carolina, Chapel Hill,
and University of Washington.
Huggett, Mark, Gustavo Ventura, and Amir Yaron. December
2011. “Sources of Lifetime Inequality.” American Economic
Review 101 (7): 2923–2954.
Ichino, Andrea, Loukas Karabarbounis, and Enrico Moretti.
January 2011. “The Political Economy of Intergenerational
Income Mobility.” Economic Inquiry 49 (1): 47–69.
Isaacs, Julia B. 2008. “Economic Mobility of Black and White
Families.” In Getting Ahead or Losing Ground: Economic
Mobility in America, edited by Julia B. Isaacs, Isabel V.
Sawhill and Ron Haskins, 71–80. Washington, D.C.: The
Pew Charitable Trusts.
Kihlstrom, Richard E., and Jean-Jacques Laffont. August
1979. “A General Equilibrium Entrpreneurial Theory
of Firm Formation Based on Risk Aversion.” Journal of
Political Economy 87 (4): 719–748.
Knight, Frank H. 1921. Risk, Uncertainty, and Profit. Hart,
Schaffner, and Marx Prize Essays, no. 31. Boston:
Houghton Mifflin.
Kopczuk, Wojciech, Emmanuel Saez, and Jae Song. February
2010. “Earnings Inequality and Mobility in the United
States: Evidence from Social Security Data since 1937.”
Quarterly Journal of Economics 125 (1): 91–128.
Restuccia, Diego, and Carlos Urrutia. December 2004.
“Intergenerational Persistence of Earnings: The Role of
Early and College Education.” American Economic Review
94 (5): 1354–1378.
Romer, Paul M. October 1986. “Increasing Returns and
Long-Run Growth.” Journal of Political Economy 94 (5):
1002–1037.
Krusell, Per, and Anthony A. Smith, Jr. October 1998. “Income
and Wealth Heterogeneity in the Macroeconomy.” Journal
of Political Economy 106 (5): 867–896.
Saez, Emmanuel. January 2013. “Striking It Richer: The
Evolution of Top Incomes in the United States (Updated
with 2011 Estimates).” Manuscript.
Lawrance, Emily C. February 1991. “Poverty and the Rate
of Time Preference: Evidence from Panel Data.” Journal
of Political Economy 99 (1): 54–77.
Sawhill, Isabel V., Scott Winship, and Kerry Searle Grannis.
September 2012. “Pathways to the Middle Class: Balancing
Personal and Public Responsibilities.” Brookings
Institution Center on Children and Families.
Lubotsky, Darren H. October 2007. “Chutes or Ladders? A
Longitudinal Analysis of Immigrant Earnings.” Journal
of Political Economy 115 (5): 820–867.
Lucas, Robert E. Jr. July 1988. “On the Mechanics of Economic
Development.” Journal of Monetary Economics 22 (1): 3–42.
Mazumder, Bhashkar. May 2008. “Upward Intergenerational
Economic Mobility in the United States.” Economic
Mobility Project, The Pew Charitable Trusts.
Mazumder, Bhashkar. April 2012. “Is Intergenerational
Mobility Lower Now than in the Past?” Federal Reserve
Bank of Chicago Chicago Fed Letter, no. 297.
Meyer, Bruce D., and James X. Sullivan. January 4, 2013.
“Consumption and Income Inequality in the U.S. since
the 1960s.” Paper presented at the Allied Social Science
Associations Annual Meeting, San Diego.
Mulligan, Casey B. 1997. Parental Priorities and Economic
Inequality. Chicago: University of Chicago Press.
Passel, Jeffrey, D’Vera Cohn, and Ana Gonzalez-Barrera.
April 2012. “Net Migration from Mexico Falls to Zero—and
Perhaps Less.” Pew Research Hispanic Center.
Piketty, Thomas, and Emmanuel Saez. February 2003.
“Income Inequality in the United States, 1913–1998.”
Quarterly Journal of Economics 118 (1): 1–39.
Schulhofer-Wohl, Sam. October 2011. “Heterogeneity and
Tests of Risk Sharing.” Journal of Political Economy 119
(5): 925–958.
Seshadri, Ananth, and Kazuhiro Yuki. October 2004. “Equity
and Efficiency Effects of Redistributive Policies.” Journal
of Monetary Economics 51 (7): 1415–1447.
Shorrocks, Anthony. December 1978. “Income Inequality
and Income Mobility.” Journal of Economic Theory 19 (2):
376–393.
Solon, Gary. 2004. “A Model of Intergenerational Mobility
Variation over Time and Place.” In Generational Income
Mobility in North American and Europe, edited by Miles Corak,
38–47. Cambridge, England: Cambridge University Press.
Stokey, Nancy L. 1996. “Shirtsleeves to Shirtsleeves: The
Economics of Social Mobility.” In Frontiers of Research in
Economic Theory: The Nancy L. Schwartz Memorial Lectures,
1983-1997, edited by Donald P. Jacobs, Ehud Kalai and
Morton I. Kamien, 210–241. Cambridge, England:
Cambridge University Press.
Sullivan, Teresa A., Elizabeth Warren, and Jay Lawrence
Westbrook. 2000. The Fragile Middle Class: Americans in
Debt. New Haven and London: Yale University Press.
Pritchett, Lant. 2006. Let Their People Come: Breaking the
Gridlock on Global Labor Mobility. Washington, D.C.: Center
for Global Development.
23
F edera l R eser v e B a n k o f R i c h m o n d
|
2012 ANNUAL REPORT